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High-resolution feature based central venous catheter tip detection network in X-ray images
Yuhan Wang1, Hak Keung Lam1, Zeng-Guang Hou2
1Department of Engineering, King's College London, Strand, London, WC2R 2LS, United Kingdom.
Medical Image Analysis
|July 9, 2023
Summary
This study introduces an AI framework using convolutional neural networks (CNNs) to automatically detect central venous catheter (CVC) tip position in X-ray images, improving accuracy and reducing clinician workload.
Area of Science:
- Medical Imaging
- Artificial Intelligence in Healthcare
- Radiology
Background:
- Central venous catheters (CVCs) are crucial for medication delivery in hospitalized patients.
- Malposition of CVCs can lead to severe complications, including death.
- Current CVC tip detection relies on manual interpretation of X-ray images by clinicians, which is labor-intensive.
Purpose of the Study:
- To develop an automated framework for precise CVC tip detection in X-ray images.
- To reduce the workload of clinicians in identifying CVC malpositions.
- To minimize the occurrence of complications arising from CVC malposition.
Main Methods:
- A convolutional neural network (CNN) based framework was designed, incorporating a modified HRNet, a segmentation supervision module, and a deconvolution module.
- The modified HRNet preserves high-resolution features for accurate localization.
- Segmentation supervision and deconvolution modules enhance feature resolution and differentiate CVCs from other structures.
Main Results:
- The proposed framework achieved a mean Pixel Error of 4.11 on a public CVC dataset.
- Performance significantly outperformed three comparative methods: Ma's method, SRPE method, and LCM method.
- The algorithm demonstrated high precision in detecting the catheter tip position.
Conclusions:
- The developed CNN-based framework offers a promising automated solution for accurate CVC tip detection in X-ray images.
- This technology can enhance patient safety by reducing CVC malposition-related risks.
- The approach has the potential to streamline radiological workflows and improve diagnostic efficiency.
